An important limitation of CNN models when trained on small datasets, such as biometric datasets, is that they tend to select features which are not generalizable due to overfitting.
For this purpose, we opted for employing MoCo-v1 as a fixed backbone network [1]} to improve generalizability of the extracted features. This network is trained on ImageNet using a contrastive loss that attempts to find similarities and dissimilarities among synthesized variants of training data samples in an unsupervised way. It is subclass of self-supervised learning at which a deep neural network is trained to solve pseudo-tasks.
As a result, the network learns to extract discriminative features at its early layers to solve the pseudo-tasks. Thus, when we use MoCo-v1 as our backbone for PAD in a continual learning setting, no input or label information of the future PA types have been used to train the feature extraction model. This property ensures that no information about the training dataset has been used in training the model. This allows to claim that the new attacks are indeed unseen. We note that extracting features using this pre-trained network leads to an separability of different types of attacks, as shown in the t-SNE visualizations of Figure REF , for PADISI-Face dataset as an example which enables our model to identify data points that belong to new attack classes. This observation demonstrates we can use the backbone model as a good feature extractor to identify OTDS.
